Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/votruongdanh/skills-agent/debuggit clone --depth 1 https://github.com/VoTruongDanh/Skills-AgentWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00053 | $0.00910 |
| Opus 5 | $0.00026 | $0.00455 |
| Sonnet 5 | $0.00011 | $0.00182 |
| Haiku 4.5 | $0.00005 | $0.00091 |
Grade A, and why
debug scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Protocol
START: Read .ai-memory.md from project root. Check for known bugs, past fixes, tech stack details, common error patterns, and architecture notes.
END: Update .ai-memory.md using Memory Compaction Rules with: bug, root cause, fix, files touched, lessons learned, and remaining risks.
Goal
Find the real root cause, not just the first visible symptom.
Agent Routing
- If bug is in API/server/backend → read
.kiro/skills/agents/agents/backend-specialist.mdand apply its knowledge - If bug is in UI/rendering/CSS → read
.kiro/skills/agents/agents/frontend-specialist.mdand apply its knowledge - If bug is in database/queries → read
.kiro/skills/agents/agents/database-architect.mdand apply its knowledge - If bug may be a security issue → read
.kiro/skills/agents/agents/security-auditor.mdand apply its knowledge - Default → read
.kiro/skills/agents/agents/debugger.mdand apply its systematic analysis
Socratic Gate
Before debugging, verify:
- What is the expected behavior vs actual behavior?
- Is there a log, stacktrace, or error message?
- When did this start? (recent change, always broken, intermittent?)
- Have we tried this before? If any answer is missing, ASK before proceeding.
Workflow
- Read Memory — Load
.ai-memory.mdfor project context and past bug history. - Summarize the bug, expected behavior, and actual behavior.
- Gather evidence from logs, stack traces, code paths, config, and recent changes.
- List the top hypotheses ranked by likelihood. Eliminate previously failed hypotheses immediately.
- Eliminate hypotheses using direct evidence.
- Identify the root cause and confirm with evidence before attempting any code changes.
- Propose an effective solution that addresses the root cause definitively. Ensure clean code standards are met and fully update all related files.
- Suggest how to verify the fix and prevent regressions.
- Quality Gate — Read
.kiro/skills/_scripts/checklist.mdand run cross-cutting quality checks. - Update Memory — Save root cause, fix, and lessons to
.ai-memory.md.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 79 lines · 53 tokens per session scan A e46680c3b149
debug is a cursor rule published in the GitHub repository VoTruongDanh/Skills-Agent (2 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 910 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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